General patient representation from electronic health records
We propose a deep learning algorithm to learn a low dimensional representation (embedding) of patients from their raw electronic health records. We evaluate our embeddings with descriptive analysis and a code assignement task. These representations would be reusable for other tasks such as patient similarity for cohort selection, information retrieval, electronic phenotyping, prediction.

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